IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

Abstract

With the rapid development of the Internet of Things technology, the use of light sensing technology combined with wireless networks can collect users’ physiological data in real time to help users better manage their health. This study aims to explore the data analysis application of wearable devices based on optical sensing and wireless networks in community fitness, so as to improve the fitness participation and health management effect of community residents. The research designed a wearable device with integrated optical sensor and wireless network function, which can monitor heart rate, blood oxygen saturation and exercise status in real time. Data is uploaded to the cloud via Bluetooth and mobile networks for storage and analysis. Community users view their own data records and analysis reports through mobile applications, and the research team processes the collected data through big data analysis methods to find the connection between fitness activities and health indicators. The results of the study showed that users of the device experienced significant improvements in fitness engagement and exercise effectiveness. The user’s heart rate and blood oxygen level remained in a healthy range over multiple fitness cycles, and the analysis results indicated that regular exercise time was positively correlated with physiological health indicators. This technology not only makes data collection more convenient, but also provides personalized health management programs for community residents and promotes the development of healthy lifestyle.